Machine Learning Models of Electrocatalysts for Ammonia Production
Abstract
Ammonia is an essential feedstock for producing fertilizers, pharmaceuticals, and other chemicals in both industry and agriculture. As such, it is imperative to build cost-effective strategies to replace the traditional nitrogen fixation process. The electrochemical nitrogen reduction reaction driven by economic catalysts is a promising strategy due to its efficiency, cleanliness, and sustainability. However, the conventional approach to discovering new catalysts relies on trial-and-error methods. Additionally, there is currently a lack of demonstrated examples for the computational design of electrochemical nitrogen reduction reaction devices to achieve practical applications. Therefore, the primary purpose of this project is to develop computational strategies for discovering novel, economic catalysts for ammonia production that exhibit high catalytic reactivity and selectivity comparable to that of nitrogenases.
This research project is supported by the FORMAS early-career grant, and initiated in January 2024 at Uppsala University. To achieve this aim, I will develop streamlined computational methods and machine learning models to aid in designing cost-effective catalysts and devices for electrochemical nitrogen reduction reactions. These predicted promising catalysts will be validated through interdisciplinary partnerships and utilized to engineer devices for electrochemical nitrogen reduction reactions. By achieving this goal, I will promote a sustainable path to agricultural feedstock production.
In undertaking the research project, I will quantitatively calculate the geometric and electronic properties of collected catalysts using benchmarked computational approaches. Density functional theory (DFT) and semiempirical approaches will be benchmarked for the quantitative calculations. These generated datasets of catalyst properties will be used for machine learning protocols. Additionally, I will develop efficient computational approaches for simulating reaction pathways of electrochemical nitrogen reduction reactions in a realistic environment. This involves comparing DFT functionals to calculate the key steps of a validated catalyst obtained from machine learning models. I will also evaluate quantum mechanics/molecular mechanics molecular dynamics and empirical valence bond methods for describing bond breaking of the catalysts in solution.
In this section of the project, I will generate a massive amount of data set from DFT and molecular dynamic simulations, these datasets will be applied for machine learning. Therefore, I will need storage resources to store these massive data sets. I have received HPC resources with the project NAISS 2024/5-22, and I am applying for storage related to this computing project.
